Automatic Card Fraud Detection Based on Decision Tree Algorithm by Elena Flondor (19249662)

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THG-OAFN: Time-aware heterogeneous graph oversampling and active fraud detection algorithm. by Sizheng Wei (22794567)

“…<p>THG-OAFN: Time-aware heterogeneous graph oversampling and active fraud detection algorithm.</p>…”

Auto Insurance Fraud Detection by M RN (9866504)

“…<p dir="ltr">Auto Insurance Fraud Detection: Dataset and BQABA algorithm python code</p>…”

Credit Card Fraud Classification Using Applied Machine Learning – A Comparative Study of 24 ML Algorithms by Kelechi Amamba (21022064)

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Fraud class histogram before oversampling. by HaiChao Du (18114974)

“…This indicates that the AE-XGB-SMOTE-CGAN algorithm has higher accuracy, true positive rate, true negative rate, and Matthew’s correlation coefficient, making it a promising method for detecting credit card fraud.…”

Fraud class histogram after oversampling. by HaiChao Du (18114974)

“…This indicates that the AE-XGB-SMOTE-CGAN algorithm has higher accuracy, true positive rate, true negative rate, and Matthew’s correlation coefficient, making it a promising method for detecting credit card fraud.…”

fraud_oracle.csv by Mohamed Ibrahim (17783625)

“…<p dir="ltr">This study leverages advanced methods and algorithms to create an automated insurance fraud detection system, using real insurance fraud data. …”

creditcard Dataset by Mohammad Shanaa (21511757)

“…</p><p dir="ltr"><b>Key Features:</b></p><ul><li>30 numerical input features (V1–V28, Time, Amount)</li><li>Class label indicating fraud (1) or normal (0)</li><li>Imbalanced class distribution typical in real-world fraud detection</li></ul><p dir="ltr"><b>Use Case:</b><br>Ideal for benchmarking and evaluating anomaly detection and classification algorithms in highly imbalanced data scenarios.…”

Flowchart data balancing algorithm (SMOTE). by Xiangting Shi (21439579)

“…This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. …”

Data before and after applying SMOTE algorithm. by Xiangting Shi (21439579)

“…This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. …”

A detailed overview of the proposed anomaly detection methodology. by Amjad Iqbal (3997619)

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The proposed work’s detailed system block diagram. by Amjad Iqbal (3997619)

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Transformer-GNN and their ensembled comparison of classification results on proposed. by Amjad Iqbal (3997619)

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Different data preprocessing phases/steps. by Amjad Iqbal (3997619)

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Deep autoencoding gaussian mixture model architecture for the proposed work. by Amjad Iqbal (3997619)

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Deep learning ensemble model architecture for the proposed work. by Amjad Iqbal (3997619)

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Comparison of classification results. by Amjad Iqbal (3997619)

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DAGMM comparison of classification results on the proposed solution. by Amjad Iqbal (3997619)

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LSTM network architecture for the proposed work. by Amjad Iqbal (3997619)

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The proposed work generic workflow. by Amjad Iqbal (3997619)

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